Optical coherence tomography for robotic brain surgery

ABSTRACT

Disclosed are methods related to guiding robotic surgery using optical coherence tomography (OCT) and computer-readable media and computer systems executing the methods. They may include receiving a series of cross-sectional slices of 3D space obtained from an OCT probe over biological tissue and processing and filtering the series of slices. The processing and filtering may include spatially smoothing the intensity values of each slice, thresholding each slice after it has been blurred, performing a connected-component analysis to identify blobs on the thresholded slice, filtering the blobs, performing edge detection, and invoking a selective median filter. The processing and filtering can be used to construct a depth map from the received series of cross-sectional slices in order to guide a robotic end effector, based on the depth map.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority from U.S. Patent Application No.62/873,705, filed Jul. 12, 2019, which is hereby incorporated byreference in its entirety for all purposes.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSOREDRESEARCH AND DEVELOPMENT

NOT APPLICABLE

BACKGROUND 1. Field of the Invention

The present application generally relates to optical coherencetomography (OCT), including its use as a sensor for guiding roboticsurgical systems. Specifically, the application is related to techniquesfor adapting OCT for real-time use in acquiring depth maps of a wetbrain surface or other tissue surface for robotic insertion ofelectrodes.

2. Description of the Related Art

Devices exist that can be implanted into biological membranes such asthe brain. In certain instances, the implantable device has abiocompatible substrate with conduits, such as electrodes, forstimulation of neurons and/or recording neuronal signals.

Brain implants require delicate control to securely insert and attach animplant and all of the respective connection points to the brain.Several challenges exist in surgically implanting a brain implant,including but not limited to avoiding vasculature, while also makingsuccessful physical and electrical connections into the brain.

International Patent Application Publication No. WO 2016/126340,published Aug. 11, 2016, discloses implantable devices that can beimplanted into the brain of a subject and used for a variety ofpurposes. The implantable device can have conduits or electrodes thatcan record or deliver stimulation, such as light, current, voltage, ordrugs.

In certain implementations, and particularly with progression of modernmedicine, surgical robots are becoming an assistive tool forimplantation procedures. Given the limited access to the brain as wellas the complex structure of the brain, computer vision for surgicalrobots becomes a problem in differentiating the varying layers of thebrain as well as discerning shadows that may be cast by surgery tools,portions of the implant, or perhaps even the surgical robot itself.

For brain implants utilizing electrodes, implantation with specificaccuracy becomes difficult spatially in terms of accommodating forunderlying background movement, such as blood flow, heart rate,breathing, and natural brain movement. This may be compounded by thepresence of a fluid membrane on the surface of the brain as well asdistinguishing between the proper depth to implant as more neurons areadded.

Leveraging the accuracy of a surgical robot is desirable in operationsinvolving delicate organs, such as the brain. There is a need in the artfor a more precise, real-time brain electrode implantation method toconnect to implantable devices.

BRIEF SUMMARY

Generally, a robotic surgery system uses optical coherence tomography(OCT) to facilitate implanting biocompatible electrodes in biologicaltissue (e.g., neurological tissue such as the brain) using roboticassemblies. Real-time OCT helps guide the robotic surgery system, whichincludes components to engage an implantable device, identify a targetimplantation site, and verify insertion. The system attaches, viarobotic manipulation, the electrode to an engagement element of aninsertion needle. The OCT illuminates the first few hundred microns ofbrain tissue with suitable wavelengths of light, obtain 2-dimensionalslices of the brain vasculature and other features, processes the slicesto find a depth map based on the known layering of brain tissue, andpresents the depth map so that the surgical robot may implant theelectrode via robotic assembly.

In utilizing OCT to facilitate implanting biocompatible electrodes, tobest ensure precision and accuracy throughout the operation, the OCTmust be adaptive to the brain's dynamic environment. As a result,filtering out and ensuring correct guidance to the implantation site areimportant to providing a successful operation.

The method of guiding robotic surgery may start with receiving a seriesof cross-sectional 3-dimensional (3D) space obtained from an opticalcoherence tomography (OCT) probe over biological tissue, with each sliceincluding a 2-dimensional array of intensity values. Next, the intensityvalues in each slice may be spatially smoothed to produce acorresponding blurred slice. Next, the blurred slice may be thresholdedto create a corresponding segmented slice. Next, a connected-componentanalysis may be performed on each segmented slice to identify blobs onthe respective segmented slice. Next, the blobs may be filtered at leaston a size of the blobs. Next, the filtered blobs may have edge detectionrun to construct a corresponding edge detection slice. Next, a selectivemedian filter may be invoked on the edge detection slices to construct adepth map of a surface of the biological tissue. Next, a robotic endeffector can be guided based on the depth map.

In some embodiments, the method may also include removing fromconsideration a segmented slice whose largest blob does not project atleast 50% across the respective segmented slice. In some embodiments,the method may remove the segmented slice whose blob does not project atleast 75% across the respective segmented slice.

In some embodiments, filtering out blobs may reject a blob correspondingto an electrical wire protruding from the biological tissue.

In some embodiments, the biological tissue may be the brain cortexcovered with pia-arachnoid complex.

In some embodiments, the spatially smoothing may include Gaussianblurring or median blurring.

In some embodiments, the thresholding may involve dynamically selectingthreshold values using Otsu's method to minimize intra-class intensityvariance.

In some embodiments, the method may include selecting the series ofslices from a larger set of OCT slices.

In some embodiments, the edge detecting may result in more than onecontinuous edge in each edge detection slice.

In some embodiments, the selective median filter may create multipledepth maps of surfaces of the tissue. In some embodiments, the methodmay include selecting a top surface depth map.

In some embodiments, a non-transitory computer-readable medium may storecomputer-executable instructions that, when executed by a processor,cause the processor to perform, and/or to instruct the components of thesystem to perform, any of the methods described above for guidingrobotic surgery.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a robotic surgery system using optical coherence tomography(OCT), according to embodiments.

FIG. 1B illustrates a close up bottom view of the system of FIG. 1A.

FIG. 2A is an overhead image of a portion of a brain, according toembodiments.

FIG. 2B is a 3D image showing surface topology of the brain of FIG. 2A.

FIG. 3 is a cross section of a head with a portion of its skull removedin accordance with an embodiment.

FIG. 4 is a stack of slices of 2-dimensional arrays of intensity valuesfrom a brain captured by optical coherence tomography (OCT), accordingto embodiments.

FIG. 5 is one of the slices of FIG. 4 .

FIG. 6 is the slice of FIG. 4 subject to a blurring filter in accordancewith an embodiment.

FIG. 7 is a segmented slice with identified blobs of the blurred sliceof FIG. 6 , according to an embodiment.

FIG. 8 is a segmented slice with filtered out blobs of the segmentedslice of FIG. 7 , according to an embodiment.

FIG. 9 is an edge detection slice based on the segmented slice of FIG. 8, according to an embodiment.

FIG. 10 is a depth map of a surface of a biological tissue constructedfrom the edge detection slice of FIG. 9 and others, according to anembodiment.

FIG. 11 is a 3-dimensional render of the depth map of FIG. 10 ,according to an embodiment.

FIG. 12A illustrates pre-implantation of an electrode in a target tissueproxy substance, according to an embodiment.

FIG. 12B illustrates lowering of a surgical robotic end effector of FIG.12A.

FIG. 12C illustrates insertion of an electrode into the target tissueproxy substance of FIG. 12A.

FIG. 12D illustrates the final thread electrode left in the targettissue proxy substance of FIG. 12A.

FIG. 13 illustrates an example of many electrodes implanted in braintissue, according to an embodiment.

FIG. 14 illustrates an OCT a slice showing intensity values with asingle thread, according to embodiments.

FIG. 15 illustrates an OCT a slice showing intensity values withmultiple threads, according to embodiments.

FIG. 16 illustrates an OCT a slice showing vasculature, vessels, andother artifacts in imaging, according to embodiments.

FIG. 17 is a method of guiding robotic surgery using optical coherencetomography, according to embodiments.

FIG. 18A illustrates an example computing system for robotic surgeryguided by computer vision using optical coherence tomography, accordingto embodiments.

FIG. 18B illustrates example components computing system for roboticsurgery guided by computer vision using optical coherence tomography,according to embodiments.

DETAILED DESCRIPTION

Optical coherence tomography (OCT) can be used in real-time control of arobotic arm for surgery. Specifically, OCT works well for determiningwhere vasculature is in the outermost layers of the brain—which aretransparent or translucent to the light used for OCT to a depth of about100 μm (microns). Additionally, OCT works through blood or other fluidsthat obfuscate the field.

Commercially available OCT visualization systems, which is used byoptometrists, are generally too slow for real-time control. For example,it takes a few seconds to scan and display portions of a patient's eye.Updates on the order of a few seconds are too slow for real-time roboticoperations, even with a sedated subject.

Because of the way certain layers and features of the meninges appear inOCT data, one can use these features to accelerate the determination ofthose features and guide a robotically guided needle or other endeffector.

System Overview

FIG. 1A illustrates an example system 100 for robotic surgicalimplantation of an electrode device, according to embodiments. FIG. 1Billustrates a close up bottom view of example system 100 for roboticsurgical implantation of an electrode, according to embodiments. In someembodiments, the entire system 100 may be associated with a robot, forexample a single robot may be integrated together with all thecomponents of system 100. In some embodiments, some sub-systems ofsystem 100 may be combined, for example a single robot may include aninserter head 102 that can also perform the functions of deviceengagement sub-system 104, and is not limited by the present disclosure.

In this example, system 100 includes an inserter head 102 and deviceengagement sub-system 104. Device engagement sub-system 104 can engageelectrodes for implantation, and inserter head 102 can perform targetingand/or insertion verification functions while implanting the electrodesin neurological tissue, as described herein below. Inserter head 102 mayalso be referred to as a targeting and/or insertion verificationsub-system, and device engagement sub-system 104 may also be referred toas an electrode stage. In some embodiments, the functions of inserterhead 102 and device engagement sub-system 104 can instead be performedby a single apparatus. For example, in some embodiments, the functionsof device engagement sub-system 104 may be performed by components ofinserter head 102. System 100 may further include ultrasonic cleaner106.

System 100 and/or sub-system 104 can contain light sources configured toilluminate the electrode device and system 100 and/or sub-system 102 cancontain light sources configured to illuminate the surgical field. Thelight sources illuminating the electrode device or an insertion needlecan produce light of wavelengths selected based on a material associatedwith the electrode device or needle, while the light sourcesilluminating the surgical field can produce light of wavelengths chosenfor imaging the target tissue. In particular, system 100 may containmultiple independent light modules, each capable of independentlyilluminating with 405 nm, 525 nm and 650 nm or white light. For example,if the implantable electrode device contains a bio-compatible substratemade from polyimide, the wavelength of the light from the light sourcemay be between 390 nm and 425 nm (e.g., 405 nm or 395 nm). In anembodiment, the light sources may include a laser and/or a lightemitting diode (LED). In an embodiment, the implantable electrode devicecan contain a bio-compatible substrate made from polyimide, polyamide,and/or another aromatic rigid chain polymer material, fluorescentmaterial, or other material, and is not limited by the presentdisclosure.

System 100 can contain cameras configured to obtain images, such asdigital photos, of the electrode device and an insertion needle, andcameras configured to obtain images of the target neurological tissue,e.g. a brain cortex. In another example, the images can include imagesof any subject relevant to robotic surgical implantation. In a typicalembodiment, the cameras can include two cameras arranged at a relativeangle (e.g., a relative angle substantially equal to 45°, or some otherangle). In various embodiments, system 100 can contain additionalcameras, or other sensors, such as video cameras, microphones, chemicalsensors, temperature sensors, time sensors, and force or pressuresensors, and is not limited by the present disclosure.

The light sources may include one or more light sources that can becycled or strobed between illuminated and extinguished states, and/oramong different wavelengths of light, so that the cameras can imagedifferent perspectives or aspects of the surgical field. In anembodiment, the cameras can be cooled in order to increase theirsensitivity, such as to faint fluorescent light. In one embodiment, oneor more of the cameras may be integrated into a microscope. Inembodiments, the light sources may be suitable for interferometry, suchas that used in optical coherence tomography.

In embodiments where the light sources are suitable for interferometry,such as that used in optical coherence tomography, a sensor may be usedfor the interferometry. The sensor may acquire and transmit data on theorder of, for example, 30 gBits/sec.

System 100 can include a processing unit, such as computing system 1800in the example of FIG. 18A below, configured to execute a computervision heuristic to process the images obtained by the cameras. Thecomputing system may be communicatively coupled to a plurality ofcameras configured to image one or more portions of the surgical fieldand/or the electrode device and needle. In particular, the computingsystem can apply computer vision techniques to images from the camerasin order to determine the location and/or orientation of the electrodedevice. In an embodiment, the computing system can determine locationsand/or orientations of an insertion needle and a target tissue forimplantation. In embodiments, the processing unit may be suitable forprocessing and extracting surface data acquired from, for example, theoptical coherence tomography data. The computing system may then processthat data. For example, the computing system can determine a contour ofthe target surgical tissue, based on images from the cameras. In variousembodiments, a processing unit can include one or more processors, oneor more processing cores, one or more computing systems such ascomputing system 1800 in the example of FIG. 18A below, one or moreGPUs, or combinations thereof, and is not limited by the presentdisclosure.

System 100 can contain one or more robotic assemblies, such as a roboticassembly configured to implant the electrode device surgically intotarget biological tissue. The robotic assemblies may be guided by aprocessing unit, such as computing system 1800 in the example of FIG.18A below, based on the triangulated locations of the electrode device,an insertion needle, and/or a target tissue, determined by the computingsystem. In an embodiment, system 100 can further contain an additionalrobotic assembly configured to attach an engagement element of theinsertion needle to a reciprocal engagement element on the electrodedevice. In an embodiment, when surgically implanting the electrodedevice, the robotic assemblies can surgically implant the insertionneedle attached to the electrode device. The robotic assemblies canfurther be guided based on images from the cameras. In an embodiment,system 100 can contain other actuators, such as sonic, ultrasonic, orpressure actuators, or can guide other implements such as a scalpel, andis not limited by the present disclosure.

In some embodiments, system 100 can include additional cameras, and isnot limited by the present disclosure. For example, system 100 can use aseparate camera system, located on a head of a robotic assembly, formapping the target tissue site. In some embodiments, this roboticassembly may also be configured to carry an insertion needle. Theseparate camera system can be movably situated on one or more axes. Inan embodiment, the system drives this robotic assembly down an axis,such that a focus of the camera system is below the target tissue siteof interest, such as brain tissue. The robotic assembly can move upwardalong the axis, and/or scan the camera system upwards, in order to imagethe target tissue.

In a typical embodiment of the present disclosure, robotic surgerysystem 100 may implant implantable devices including electrodes withimproved depth penetration that are able to penetrate below the surfaceof biological tissue (e.g., cortex). Example electrodes may includethose discussed in a U.S. Patent Publication No US 2020/0085375 A1titled “Electrode Design and Fabrication,” which is hereby incorporatedby reference. The disclosed robotic system may implant implantabledevices that are arranged in a pillbox, a cartridge, and/or apillbox-cartridge assembly such as those discussed in a U.S. PatentPublication No. US 2020/0086111 A1 titled “Device Implantation Using aCartridge,” which is hereby incorporated by reference. Additionally, thedisclosed robotic system may control the operation of a needle.

FIG. 1B shows a bottom view of an example system 100. The view shows anOCT sensor 110. The OCT sensor 110 may be positioned such to receivelight signals from the back of a tissue sample. In embodiments, the OCTsensor 110 may be positioned to follow the inserter head 102, such thatas the inserter head 102 operates on a particular region of the brain,the OCT sensor 110 is receiving visual data regarding the operatingregion.

FIG. 2A is an image of a portion of a brain, including target insertionareas in accordance with an embodiment. One of the goals of some brainsurgeries is to avoid vasculature, such as vasculature 210, the darkspidery regions within the photographs. A doctor may use these images tomanually select where to insert needles in order to avoid vasculature.Automating this selection process and targeting can enable upscaling tohundreds or thousands of locations.

FIG. 2B is a volume snapshot of the brain showing vessels andvasculature. The surface of the brain is a dynamic environment withpulsating vessels, flowing fluid, and pulsing layers. Due to thecomplexity of the environment, surgery and implantation via a roboticsystem must adapt for the factors that may impact precision andaccuracy. The image shown in the figure depicts the surface of thebrain. Region 252 is a blood vessel, region 254 is the subarachnoidspace, the region 256 shows the brain surface undulating. Even with amapping of the brain, given its dynamic, changing surface, processingand understanding the surface at speeds quick enough to instruct aninserter head, such as inserter head 102 (see FIG. 1A), may provechallenging.

FIG. 3 is a cross-sectional view of a mammalian brain. In oneembodiment, the methods includes forming an opening in the scalp andcutting a hole through the skull and through the dura mater prior toimplantation. The dura mater is made up of two layers known as theperiosteum and the meningeal, which are generally referred to as onlyone layer or the dura layer. Next is the arachnoid layer. The arachnoidlayer is a thin membrane that surrounds the brain and is separable fromthe dura. There is a space between the dura and the arachnoid membranethat is called the subdural space.

Below the arachnoid layer is the subarachnoid space, which is limitedexternally by a water-tight layer of connective tissue, the arachnoid,and internally by a thinner layer, the pia mater. It is within thesubarachnoid space that CSF flows.

The pia mater adheres intimately to the surface of the brain and spinalcord. The pia mater is the layer of meninges closest to the surface ofthe brain. The pia mater has many blood vessels that reach deep into thesurface of the brain. The major arteries supplying the brain provide thepia with its blood vessels. The space that separates the arachnoid andthe pia mater is called the subarachnoid space.

Real-Time Imaging Process

Optical coherence tomography (OCT) can be used to guide a system, suchas system 100 (see FIG. 1A), in surgically implanting electrodes. Due tothe brain's liquid-surface layer, flowing blood, CSF, and varying layersthat may cause obscuring of the light path, accurate depth of field mapsthat can quickly be generated to guide an inserter head are largelybeneficial in ensuring precision of a surgical robot system.

In ensuring a proper depth map is generated, an OCT sensor, such as theOCT sensor 110, may begin by obtaining a stack of 2-dimensional arrays,showing intensity values that correlate to light that bounces back tothe sensor of the system. In embodiments, the OCT sensor may obtain avolume of points of light intensity based off a captured reflection froma surface, such as a brain. The OCT sensor may obtain a range of points,such as 20 million, 30 million, 40 million or more points. The variouspoints are collected into a 2-dimensional array of slices, with eachslice being a different depth layer of the brain. The 2-dimensionalarray may consist of anywhere from 120-160 slices. From this full stackof slices, a processor, such as the processor 1800 of FIG. 18A, maynarrow down and select from 10 to 30 slices based off of spatialselection received for targeted insertion cites.

FIG. 4 shows a stack 400 of slices of 2-dimensional arrays of intensityvalues from a brain, captured by OCT, such as by the OCT sensor 110shown in FIG. 1B. The stack 400 consists of slices 410, with each slice410 showing the points collected by the OCT sensor. The OCT sensor wouldbe at the top of the figure facing downward. The various points arecollected into a 2-dimensional array of slices, forming a stack, such asstack 400, with each slice being a different depth layer of the brain.For example, the slice 410 on the rightmost side of the stack can show aslice imaged by OCT at a particular depth.

FIG. 5 shows an exemplary slice 410 of the stack 400. The slice 410 mayhave an array of intensity values as measured by OCT. Each slice 410shows a cross-section of the brain along the X-Y plane, with the OCTsensor looking down from the top. Thus, a stack of slices would form adepth of the brain with corresponding intensity values along the depth.

However, OCT may pick up background signal, noise and other artifacts inthe captured slice 410. In order to filter out low signal and noise, asmoothing method may be applied.

A processing unit, such as processing unit 1800 (see FIG. 18A), may useprocessing and filtering methods on each slice to generate a depth mapthat guides a surgical robot in implanting operations.

As the intensity points represent discrete locations on each slice, aprocessing unit may encounter problems identifying regions that formpart of the surface as opposed to voids, such as noise, low signal, orartifacts. In order to correct for this, a processing unit may implementa smoothing filter to obtain a smoother image relative to the array ofintensity points. In embodiments, the smoothing can be a Gaussian blur,a median blur, a bilateral filter, or other spatially smoothingalgorithms. The processing unit may apply the filter to each sliceindividually within a stack.

FIG. 6 shows a spatially smoothed, blurred slice 600. The blurred slice600 may be processed from the slice 410. For example, the blurred slice600 has a Gaussian blur applied to smooth out the intensity values. Ascan be seen in blurred slice 600, as compared to slice 410 of FIG. 5 ,the intensity is smoother and forms a blurred image of the slice.

After applying a smoothing operation, a processing unit, such asprocessing unit 1800 (see FIG. 18A), may perform a thresholdingoperation to create a corresponding segmented slice. The thresholdingmay be based off of the pixel intensities of the blurred slice, such asblurred slice 600. The thresholding operation may be a k-means method ora suitable thresholding operation to distinguish groups based off ofpixel intensity.

Thresholding can involve dynamically selecting threshold values usingOtsu's method to minimize intra-class intensity variance, orequivalently, maximize inter-class intensity variance.

FIG. 7 shows corresponding segmented slice 700. Corresponding segmentedslice 700 may come from a blurred slice, such as blurred slice 600, thathas had a thresholding operation applied. For example, in a k-meansmethod that thresholds into two groups based off of intensity, pixelintensities over a threshold value may be represented as white spotswhereas pixel intensities below a threshold value become darkened. As aresult, the image becomes a 2-dimensional image of regions withthresholded pixel intensities.

The threshold intensity may be based off of known pixel intensities fora measured brain surface. For example, a known measured brain surfaceintensity may be used as the thresholding value for an unknown brain togauge the pixel intensity of the surface of the brain.

Comparing the corresponding segmented slice 700 with the blurred slice600, there is a visual difference between the gradient intensity valuesof blurred slice 600 and the thresholded white on black of thecorresponding segmented slice 700. For example, the lower intensitypixels close to the bottom of blurred slice 600 become empty/darkpatches, providing a clear image of the regions with higher intensitysignal that correspond to portions of the surface of the brain.

After a thresholding operation, the processing unit may use aconnected-component analysis/operation to further define the surface ofthe brain. The connected-component operation groups connected regionsbased on the corresponding segmented slice. The connected componentoperation may look for continuity in the corresponding segmented sliceand identify “blobs,” or regions of threshold intensity values that formcontinuous structures. The processing unit may look at continuity toevaluate whether a region fulfills the qualifying pixel size of a blob.For example, the processing unit may look at each slice and set athreshold continuity of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90%of the slice or more to qualify as a blob above the threshold value.

The continuity threshold may be based off of known brain heuristics andto assist in filtering out noise and artifacts that were captured by theOCT in processing each slice. For example, noisy signal and occlusionscast from the liquid or shadows would not have a continuity of greaterthan 50% of the slice, and thus, are subject to filtering out. Inembodiments, the continuity threshold may be 60%, 70%, 80%, or 90%, orotherwise to properly filter out unwanted blobs.

FIG. 8 is an example of a slice 800 with connected-component analysisperformed on the corresponding segmented slice 700. As shown in thefigure, slice 800 has blobs 801, 802, 803, 804, and 805. For example,blob 801 is a region of the brain corresponding to the surface. As blob801 spans over 50% of the slide width, blob 801 would satisfy thethreshold continuity length. However, blob 802, blob 803, blob 804, andblob 805 would be filtered out due to their blob sizes not satisfyingthe threshold continuity length of the slice 800.

After filtering the blobs based on a size, the processing unit mayperform an edge detection on the blobs that have been filtered based onsize.

FIG. 9 shows an edge detection slice 900 that shows the result of anedge detection algorithm run on the blobs of slice 800. Blob 801 isabove the threshold blob size, and thus, blob 801 is suitable for theedge detection algorithm. Slice 900 shows edge 901 that corresponds tothe top edge of blob 801.

For speed, the edge detection algorithm may proceed only from the top ofthe slice to the bottom (i.e., in they direction only) instead of a morecomputationally expensive edge detection algorithms that finds edgesfrom all angles.

The resultant edge 901 details the edge corresponding to the brainsurface at a particular stack depth within the stack of slices. Aprocessing unit may run a smoothing operation, thresholding operation, aconnected component analysis, filtering, and edge detection on multipleslices in the stack in order to acquire the surface edge of the brain atvarying depths corresponding to each slice.

From the resultant edges of the blobs of the stack of slices, a surfacetopology can be extracted using image derivatives and compiled into a32-bit depth map. The slices can be ranked spatially according to knowndepths from the OCT measurement. The detected edges can be correlated tothe real-world coordinates on the actual brain surface.

FIG. 10 shows a depth map 1000 of a surface topology based off of stack400, according to embodiments. The depth map 1000 features the brainsurface 1002 and a portion of the skull 1004. As shown in depth map1000, the surface 1002 can be seen along the bottom of the depth map,which can help guide a robot inserter head, such as robot inserter head102, in implanting electrodes to a surface of the brain. Furthermore, asan example, because the processing unit is aware of the portion of theskull 1004 to the right, the guidance of the robot inserter head cansteer clear of potentially damaging the threads on bone. However, justthe image derivative can produce noise and may not account for shadowsof other electrodes or threads currently in the brain. These artifactscan present a false surface of the brain much higher than the actualsurface of the brain.

In order to filter out any noise or shadows that provide an inaccuratedepth map, a selective median filter can be used. The selective medianfilter may identify median points along the formed edges to create asurface topography. Moreover, because the selective median filterassesses the median value, the processing unit can confirm that a validsurface location is being selected, rather than a location in space thatmay occur with a selective mean filter.

FIG. 11 shows a 3-D depth map 1100 of a brain surface after a selectivemedian filter has been run on the depth map 1000 and several other depthmaps. The 3-D depth map 1100 shows the brain surface 1102. The ridge onthe right is the portion of the skull 1104. Based on the constructeddepth map 1100, the processing unit can guide a robotic inserter head,such as inserter head 102, to implanting electrodes correctly along thesurface of the brain.

Electrode Implantation

FIGS. 12A-12D illustrate implantation of electrodes in a target tissueproxy substance 1208, according to an embodiment. In particular, FIG. 12shows a sequence of steps of the insertion process by an end effector ofinserter head 1202 into an agarose brain tissue proxy. In this example,a needle 1203 first inserts a first thread 1205 on the left, which canhold a plurality of electrodes (e.g., 32 electrodes), and then inserts asecond thread 1204 (shown in work), holding a second plurality ofelectrodes. The implantation can be guided by images acquired by OCT anda processing unit that obtains real-time data on the locations of thesurface of implantation.

The inserter head 1202 support system holds an imaging stack used forguiding the needle into the thread loop, insertion targeting, liveinsertion viewing, and insertion verification. In addition, the inserterhead contains light modules, each capable of independently illuminatingwith 405 nm, 525 nm and 650 nm or white light. A 405 nm illumination canexcite fluorescence from polyimide and allow the optical stack andcomputer vision to reliably localize the (16×50) μm² thread loop andexecute sub-micron visual serving to guide, illuminated by 650 nm theneedle through it. Stereoscopic cameras, computer vision methods such asmonocular extended depth of field calculations, and illumination with525 nm light can allow for precise estimation of the location of thecortical surface while avoiding vasculature and other threads that mayhave been previously implanted.

The robot registers insertion sites to a common coordinate frame withlandmarks on the skull, which, when combined with depth tracking,enables precise targeting of anatomically defined brain structures.Integrated custom computer instructions may allow pre-selection of allinsertion sites, enabling planning of insertion paths optimized tominimize tangling and strain on the threads. The planning featurehighlights the ability to avoid vasculature during insertions, one ofthe key advantages of inserting electrodes individually. This mayprovide a technical advantage, in order to avoid damage to theblood-brain barrier and thereby reduce inflammatory response. In anembodiment, the robot can feature an auto-insertion mode. While theentire insertion procedure can be automated, a surgeon can retaincontrol, and can make manual micro-adjustments to the thread positionbefore each insertion into the target tissue, such as a cortex. Theneurosurgical robot is compatible with sterile shrouding, and hasfeatures to facilitate successful and rapid insertions such as automaticsterile ultrasonic cleaning of the needle.

FIG. 13 illustrates an example of electrodes implanted in brain tissue,according to an embodiment. In a typical example, the disclosed systemand methods may implant 96 polymer threads, such as threads 1308, intotarget tissue, each thread with 32 electrodes, for a total of 3,072electrodes in the array. The electrodes are designed to be compact,thin, and flexible, with from 5 to 50 μm thread width and nominal threadthickness of 4 to 6 μm. In a typical example, the thread length can beapproximately 20 mm. The small size and increased flexibility of theseprobes offers greater biocompatibility, enabling the probes to remainimplanted for long periods of time without triggering immune responses.The small thread cross-sectional area can also minimize tissuedisplacement in the target.

Given the number of threads being inserted in a typical implantationoperation, several of the initially inserted threads may be prone tocasting shadows that impact computer vision for later inserted threads.For example, as can be seen in FIG. 13 , the numerous threads 1308 arein close proximity with each other. During the course of implantation,previous threads must be accounted for by the processing unit in guidingthe inserter head. While the example of FIGS. 5-9 depict a clean imageof the brain surface, examples of the complexities of the environment ofthe brain can be seen below in FIGS. 14-16 .

FIG. 14 shows a slice 1400 with intensity points measured by an OCTsensor, such as OCT sensor 110 (see FIG. 1B), with a thread casting ashadow across the brain surface. The slice 1400 shows regions 1401,1402, and 1403. Region 1401 is the surface of the brain. The surfacespanning across the slice would thus be the target of implantation.However, region 1402 represents a thread that casts a shadow. As can beseen by the drop in intensity at the region 1403, the thread in region1402 impacts what is measured by the OCT sensor below. Due to theshadows cast, the resultant blob may be an inaccurate portrayal of thesurface of the brain at that particular depth.

FIG. 15 shows a slice 1500 with intensity points measured by an OCTsensor, such as OCT sensor 110 in FIG. 1B, with multiple threads castingshadows across the brain surface. The slice 1500 shows regions 1501,1502, 1503, and 1504. Region 1501 is the surface of the brain. Theregion 1502 indicates multiple threads protruding from the surface ofthe brain. As can be seen by the drop in pixel intensity at the region1503, the threads literally cast a shadow over the data below itacquired from the OCT sensor.

The artifacts observed at the region 1504 are caused by the refractionof light off of the fluid of the brain. The light sources used for OCTmay refract off of the fluid on the brain when at certain angles,causing artifacts that are picked up as intensity values above thesurface layer of the brain.

FIG. 16 shows a slice 1600 with intensity points measured by an OCTsensor, such as OCT sensor 110 in FIG. 1B, with vessels being imaged bythe OCT sensor. The slice 1600 shows regions 1601, 1603, 1606, 1607, and1608. Region 1601 is the surface of the brain. The shadows at region1603 are occlusions cast from fluid and vessels along the surface of thebrain. The high intensity regions 1606 represent blood vessels along thebrain. The dark void region 1607 represents CSF flowing along the brain,which the OCT sensor have trouble picking up. The region 1608collectively is the arachnoid space of the brain. The figure highlightsthe varying complexities that make using OCT data alone, withoutprocessing, a less precise metric for surgical operations in guiding asurgical robot.

Accordingly, use of the aforementioned filtering and processing methodsimproves the precision and accuracy of a surgical robot in implantingelectrodes to neurons. Moreover, the above mentioned filtering andprocessing methods can be quick and efficient enough that a processedimage will not have changed by the time the depth map has beengenerated. The filtering methods may not require lengthy processes todivide the data points, and the depth map generation does not requirelengthy computation times.

The use of a selective median filter on the depth map allows for noiseand artifacts, like those shown above, that may make it into the edgedetection algorithm to be accounted for. In particular, when multipleelectrodes are being implanted, it is important to avoid the previouslyimplanted electrodes, and also important that previously implantedelectrodes do not present a false surface mapping of the surface of thebrain. Such a guiding error could result in implantation failure as wellas breaking of the threads during operation.

Methods of Guiding Robotic Surgery

FIG. 17 is a flow chart illustrating an exemplary process 1700 forguiding robotic surgery, according to embodiments. In embodiments, themethod can be used to guide a surgical robot to implant an electrodedevice within biological tissue. The step number of “first,” “second,”etc. is illustrative and not limiting to order or the exclusion ofintermediate steps.

In a first step 1702, a series of cross-sectional slices of 3D spaceobtained from an OCT probe or sensor, such as the stack 400 of FIG. 4 ,is received, with each individual slice having a 2D array of lightintensity values. The OCT probe or sensor can be the OCT sensor 110 inFIG. 1B. An exemplary slice can be seen as the slice 410 in FIG. 5 .

In a second step 1704, the intensity values of each slice is spatiallysmoothed to produce a corresponding blurred slice. For example, thecorresponding blurred slice 600 in FIG. 6 can be the slice 410 after ithas been spatially smoothed.

In embodiments, the smoothing may be by a Gaussian blur. In embodiments,different smoothing functions, such as median blur, bilateral filter, orotherwise, can be applied to spatially smooth the slice.

In a third step 1706, each blurred slice is thresholded to create acorresponding segmented slice. Segmented slice 700 in FIG. 7 is anexemplary segmented slice, with a thresholding operation applied toidentify pixel intensities received by the OCT sensor that are over aknown value. The known pixel intensity may be based off of empiricaldata, known heuristics regarding the intensity of measured brain tissue,or otherwise.

In a fourth step 1708, a connected-component analysis is performed oneach segmented slice to identify blobs, like the slice 800 in FIG. 8 .The connected-component analysis examines connected components from thecorresponding segmented slice, examining pixel relations after they havebeen thresholded. In embodiments, performing the connected-componentanalysis can provide for spatial information regarding the surface ofthe brain at a particular depth.

In a fifth step 1710, blobs are filtered out on each segmented slicebased on at least the size of the blobs. In embodiments, the blob sizecan be known based off of empirical data from known measured blobs. Inembodiments, the filtering can be based on the continuity of a blob inproportion to the width of a particular slice. For example, blobs may befiltered off of 50%, 60%, 70%, 80%, or 90% continuity across a slice, orin other proportions as applications allow.

In embodiments, the filtering can be based on the continuity of a blobin proportion to the width of a particular slice. For example, blobs maybe filtered off of 50%, 60%, 70%, 80%, or 90% continuity across a slice.Optionally, the filtering process may remove a blob that does notproject at least 50%, 75%, or 90% across a segmented slice.

In a sixth step 1712, edge detection is performed on the filtered blobson each segmented slice, for example, like the edge detection slice 900of FIG. 9 . The edge detection identifies an edge of each blob that wasnot filtered out in previous step 1710.

In a seventh step 1714, a selective median filter is invoked on the edgedetection slices to construct a depth map of a surface of the biologicaltissue, like the depth map 1000 of FIG. 10 . The edge detection of eachslice in a stack, such as stack 400, may be put together to form a depthmap, such as depth map 1000, showing the depth map of a region of thebrain. In embodiments, a system, such as system 100 (see FIG. 1A), maybe able to relate the coordinates of a stack to its tissue location. Theselective median filter may provide for a median value of the surface,which may aid in filtering out noise and artifacts from the sensor,occlusion from fluid, or other threads already implanted in the brain.

In an eighth step 1716, a robotic end effector is guided based on thedepth map. In embodiments, the robotic end effector is controlled by asurgical robot, such as that of system 100 (see FIG. 1A). The depth mapmay guide the surgical robot in accurately and precisely moving to adesired region of the brain. In embodiments, this may be guiding aninserter head, such as inserter head 102.

FIG. 18A illustrates components of an example computing system 1800,according at least one example. Computing system 1800 can include one ormore display devices such as display devices 1802. The display devices1802 may be any suitable devices capable of visually presentinginformation. Examples of such devices may include cathode ray tube (CRT)displays, light-emitting diode (LED) displays, electroluminescentdisplays (ELD), electronic paper, plasma display panels (PDP), liquidcrystal displays (LCD), organic light-emitting diode (OLED) displays,surface-conduction electron-emitter displays (SED), field emissiondisplays (FED), projectors (LCD, CRT, digital light processing (DLP),liquid crystal on silicon (LCoS), LED, hybrid LED, laser diode), and anyother suitable device capable of displaying information.

Computing system 1800 may include computing device 1804, which may beconnected to the robotic assemblies 1820, light sources 1822, andcameras 1824, as well as to any other devices, such as actuators, etc.The computing device 1804 may be in communication with these devicesand/or other components of the robotic surgery system via one or morenetwork(s), wired connections, and the like. The network may include anyone or a combination of many different types of networks, such as cablenetworks, the Internet, wireless networks, cellular networks, radionetworks, and other private and/or public networks.

Turning now to the details of the computing device 1804, the computingdevice 1804 may include at least one memory 1814 and one or moreprocessing units (or processor(s)) 1810. The processor(s) 1810 may beimplemented as appropriate in hardware, computer-executableinstructions, software, firmware, or combinations thereof. For example,the processor(s) 1810 may include one or more general purpose computers,dedicated microprocessors, or other processing devices capable ofcommunicating electronic information. Examples of the processor(s) 1810include one or more application-specific integrated circuits (ASICs),field programmable gate arrays (FPGAs), digital signal processors (DSPs)and any other suitable specific or general purpose processors.

Computer-executable instruction, software, or firmware implementationsof the processor(s) 1810 may include computer-executable ormachine-executable instructions written in any suitable programminglanguage to perform the various functions described. The memory 1814 mayinclude more than one memory and may be distributed throughout thecomputing device 1804. The memory 1814 may store program instructions(e.g., a triangulation module 1818) that are loadable and executable onthe processor(s) 1810, as well as data generated during the execution ofthese programs. Depending on the configuration and type of memoryincluding the triangulation module 1818, the memory 1814 may be volatile(such as random access memory (RAM)) and/or non-volatile (such asread-only memory (ROM), flash memory, or other memory). In anembodiment, the triangulation module 1818 may receive and/or adjust thelinear combination coefficients for Laplacian estimation based on thepotentials measured by the CRE. In an embodiment, triangulation module1818 may implement the linear combination based on these coefficients.The computing device 1804 may also include additional removable and/ornon-removable storage 1806 including, but not limited to, magneticstorage, optical disks, and/or tape storage. The disk drives and theirassociated computer-readable media may provide non-volatile storage ofcomputer-readable instructions, data structures, program modules, andother data for the computing devices. In some implementations, thememory 1814 may include multiple different types of memory, such asstatic random access memory (SRAM), dynamic random access memory (DRAM),or ROM. The memory 1814 may also include an operating system 1816.

The memory 1814 and the additional storage 1806, both removable andnon-removable, are examples of computer-readable storage media. Forexample, computer-readable storage media may include volatile ornon-volatile, removable, or non-removable media implemented in anysuitable method or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. As used herein, modules may refer to programming modulesexecuted by computing systems (e.g., processors) that are part of thetriangulation module 1818. The modules of the triangulation module 1818may include one or more components, modules, and the like. For example,triangulation module 1818 may include modules or components thattriangulate the location of objects such as electrodes, insertionneedles, and/or target tissue based on computer vision. The computingdevice 1804 may also include input/output (“I/O”) device(s) and/or ports1812, such as for enabling connection with a keyboard, a mouse, a pen, avoice input device, a touch input device, a display, speakers, aprinter, or other I/O device. The I/O device(s) 1812 may enablecommunication with the other systems of the robotic surgery system.

The computing device 1804 may include a user interface 1808. The userinterface 1808 may be utilized by an operator or other authorized usersuch as the user to access portions of the computing device 1804 (e.g.,the triangulation module 1818). In some examples, the user interface1808 may include a graphical user interface, web-based applications,programmatic interfaces such as application programming interfaces(APIs), or other user interface configurations.

FIG. 18B illustrates examples of components of a computer system 1850,according to at least one example. The computer system 1850 may be asingle computer such as a user computing device and/or can represent adistributed computing system such as one or more server computingdevices.

The computer system 1850 may include at least a processor 1852, a memory1854, a storage device 1856, input/output peripherals (I/O) 1858,communication peripherals 1185, and an interface bus 1862. The interfacebus 1862 is configured to communicate, transmit, and transfer data,controls, and commands among the various components of the computersystem 1850. The memory 1854 and the storage device 1856 includecomputer-readable storage media, such as Radom Access Memory (RAM), ReadROM, electrically erasable programmable read-only memory (EEPROM), harddrives, CD-ROMs, optical storage devices, magnetic storage devices,electronic non-volatile computer storage, for example Flash® memory, andother tangible storage media. Any of such computer-readable storagemedia can be configured to store instructions or program codes embodyingaspects of the disclosure. The memory 1854 and the storage device 1856also include computer-readable signal media. A computer-readable signalmedium includes a propagated data signal with computer-readable programcode embodied therein. Such a propagated signal takes any of a varietyof forms including, but not limited to, electromagnetic, optical, or anycombination thereof. A computer-readable signal medium includes anycomputer-readable medium that is not a computer-readable storage mediumand that can communicate, propagate, or transport a program for use inconnection with the computer system 1850.

Further, the memory 1854 includes an operating system, programs, andapplications. The processor 1852 is configured to execute the storedinstructions and includes, for example, a logical processing unit, amicroprocessor, a digital signal processor, and other processors. Thememory 1854 and/or the processor 1852 can be virtualized and can behosted within another computing system of, for example, a cloud networkor a data center. The I/O peripherals 1858 include user interfaces, suchas a keyboard, screen (e.g., a touch screen), microphone, speaker, otherinput/output devices, and computing components, such as graphicalprocessing units, serial ports, parallel ports, universal serial buses,and other input/output peripherals. The I/O peripherals 1858 areconnected to the processor 1852 through any of the ports coupled to theinterface bus 1862. The communication peripherals 1185 are configured tofacilitate communication between the computer system 1850 and othercomputing devices over a communications network and include, forexample, a network interface controller, modem, wireless and wiredinterface cards, antenna, and other communication peripherals.

The terms “computing system” and “processing unit” as used herein areintended for all purposes to be interpreted broadly and is defined forall uses, all devices, and/or all systems and/or systems in thisdisclosure as a device comprising at least a central processing unit, acommunications device for interfacing with a data network, transitorycomputer-readable memory, and/or a non-transitory computer-readablememory and/or media. The central processing unit carries out theinstructions of one or more computer programs stored in thenon-transitory computer-readable memory and/or media by performingarithmetical, logical, and input/output operations to accomplish inwhole or in part one or more steps of any method described herein. Acomputing system is usable by one or more users, other computing systemsdirectly and/or indirectly, actively and/or passively for one or moresuitable functions herein. The computing system may be embodied ascomputer, a laptop, a tablet computer, a smartphone, and/or any othersuitable device and may also be a networked computing system, a server,or the like. Where beneficial, a computing system can include one ormore human input devices such as a computer mouse and/or keyboard andone or more human interaction device such as one or more monitors. Acomputing system may refer to any input, output, and/or calculatingdevice associated with providing an experience to one or more users.Although one computing system may be shown and/or described, multiplecomputing systems may be used. Conversely, where multiple computingsystems are shown and/or described, a single computing device may beused.

A “pia-arachnoid complex” typically includes arachnoid and pia mater.Arachnoid includes arachnoid mater and subarachnoid space containingcerebrospinal fluid (CSF). The pia mater is an approximately single-celllayer conformal to the cortex.

It is known that there are shadows from vasculature, so that informationis used to match potential vascular boundaries in one image to potentialvascular boundaries in other images. It is also known that three othersurfaces, including the CSF, pia, and cortex surfaces, should bepresent. A real-time algorithm can use this information to pre-determineareas that should have surfaces and thus narrow down their search.

It should be appreciated that a brain implant or other system and arespective control system for the brain implant can have one or moremicroprocessors/processing devices that can further be a component ofthe overall apparatuses. The control systems are generally proximate totheir respective devices, in electronic communication (wired orwireless) and can also include a display interface and/or operationalcontrols configured to be handled by a user to monitor the respectivesystems, to change configurations of the respective systems, and tooperate, directly guide, or set programmed instructions for therespective systems, and sub-portions thereof. Such processing devicescan be communicatively coupled to a non-volatile memory device via abus. The non-volatile memory device may include any type of memorydevice that retains stored information when powered off. Non-limitingexamples of the memory device include electrically erasable programmableread-only memory (“ROM”), flash memory, or any other type ofnon-volatile memory. In some aspects, at least some of the memory devicecan include a non-transitory medium or memory device from which theprocessing device can read instructions. A non-transitorycomputer-readable medium can include electronic, optical, magnetic, orother storage devices capable of providing the processing device withcomputer-readable instructions or other program code. Non-limitingexamples of a non-transitory computer-readable medium include (but arenot limited to) magnetic disk(s), memory chip(s), ROM, random-accessmemory (“RAM”), an ASIC, a configured processor, optical storage, and/orany other medium from which a computer processor can read instructions.The instructions may include processor-specific instructions generatedby a compiler and/or an interpreter from code written in any suitablecomputer-programming language, including, for example, C, C++, C #,Java, Python, Perl, JavaScript, etc.

While the above description describes various embodiments of theinvention and the best mode contemplated, regardless how detailed theabove text, the invention can be practiced in many ways. Details of thesystem may vary considerably in its specific implementation, while stillbeing encompassed by the present disclosure. As noted above, particularterminology used when describing certain features or aspects of theinvention should not be taken to imply that the terminology is beingredefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

In some embodiments, the systems and methods of the present disclosurecan be used in connection with neurosurgical techniques. However, oneskilled in the art would recognize that neurosurgical techniques are anon-limiting application, and the systems and methods of the presentdisclosure can be used in connection with any biological tissue.Biological tissue can include, but is not limited to, the brain, muscle,liver, pancreas, spleen, kidney, bladder, intestine, heart, stomach,skin, colon, and the like.

The systems and methods of the present disclosure can be used on anysuitable multicellular organism including, but not limited to,invertebrates, vertebrates, fish, bird, mammals, rodents (e.g., mice,rats), ungulates, cows, sheep, pigs, horses, non-human primates, andhumans. Moreover, biological tissue can be ex vivo (e.g., tissueexplant), or in vivo (e.g., the method is a surgical procedure performedon a patient).

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the invention. Some alternativeimplementations of the invention may include not only additionalelements to those implementations noted above, but also may includefewer elements. Further any specific numbers noted herein are onlyexamples; alternative implementations may employ differing values orranges, and can accommodate various increments and gradients of valueswithin and at the boundaries of such ranges.

References throughout the foregoing description to features, advantages,or similar language do not imply that all of the features and advantagesthat may be realized with the present technology should be or are in anysingle embodiment of the invention. Rather, language referring to thefeatures and advantages is understood to mean that a specific feature,advantage, or characteristic described in connection with an embodimentis included in at least one embodiment of the present technology. Thus,discussion of the features and advantages, and similar language,throughout this specification may, but do not necessarily, refer to thesame embodiment. Furthermore, the described features, advantages, andcharacteristics of the present technology may be combined in anysuitable manner in one or more embodiments. One skilled in the relevantart will recognize that the present technology can be practiced withoutone or more of the specific features or advantages of a particularembodiment. In other instances, additional features and advantages maybe recognized in certain embodiments that may not be present in allembodiments of the present technology.

What is claimed is:
 1. A method of guiding robotic surgery, the methodcomprising: receiving a series of cross-sectional slices of3-dimensional (3D) space obtained from an optical coherence tomography(OCT) probe over biological tissue, each slice of the series including a2-dimensional array of intensity values; spatially smoothing theintensity values in each slice to produce a corresponding blurred slice;thresholding each blurred slice to create a corresponding segmentedslice; performing a connected-component analysis of each segmented sliceto identify blobs on said segmented slice; determining a span for eachblob, the span measuring a width across a respective segmented slicethat the blob projects; filtering out blobs on each segmented slicebased on the spans of the blobs; edge detecting the filtered blobs oneach segmented slice to construct a corresponding edge detection slice;invoking a selective median filter on the edge detection slices in orderto construct a depth map of a surface of the biological tissue; andguiding a robotic end effector based on the depth map.
 2. The method ofclaim 1 further comprising: removing from consideration a segmentedslice whose largest blob does not project at least 50% across saidsegmented slice.
 3. The method of claim 2 wherein the largest blob doesnot project at least 75% across said segmented slice.
 4. The method ofclaim 1 wherein the filtering out rejects a blob corresponding to anelectrical wire protruding from the biological tissue.
 5. The method ofclaim 1 wherein the biological tissue is brain cortex covered withpia-arachnoid complex.
 6. The method of claim 1 wherein the spatiallysmoothing includes Gaussian blurring or median blurring.
 7. The methodof claim 1 wherein the thresholding involves dynamically selectingthreshold values using Otsu's method to minimize intra-class intensityvariance.
 8. The method of claim 1 further comprising: selecting theseries of slices from a larger set of OCT slices.
 9. The method of claim1 wherein the edge detecting results in more than one continuous edge ineach edge detection slice.
 10. The method of claim 1 wherein theselective median filter creates multiple depth maps of surfaces of thebiological tissue, the method further comprising: selecting a topsurface depth map for guiding the robotic end effector.
 11. Amachine-readable non-transitory medium embodying information for guidingrobotic surgery, the information indicative of instructions for causingone or more machines to perform operations comprising: receiving aseries of cross-sectional slices of 3-dimensional (3D) space obtainedfrom an optical coherence tomography (OCT) probe over biological tissue,each slice of the series including a 2-dimensional array of intensityvalues; spatially smoothing the intensity values in each slice toproduce a corresponding blurred slice; thresholding each blurred sliceto create a corresponding segmented slice; performing aconnected-component analysis of each segmented slice to identify blobson said segmented slice; determining a span for each blob, the spanmeasuring a width across a respective segmented slice that the blobsprojects; filtering out blobs on each segmented slice based on the spansof the blobs; edge detecting the filtered blobs on each segmented sliceto construct a corresponding edge detection slice; invoking a selectivemedian filter on the edge detection slices in order to construct a depthmap of a surface of the biological tissue; and guiding a robotic endeffector based on the depth map.
 12. The machine-readable medium ofclaim 11 further comprising: removing from consideration a segmentedslice whose largest blob does not project at least 50% across saidsegmented slice.
 13. The machine-readable medium of claim 12 wherein thelargest blob does not project at least 75% across said segmented slice.14. The machine-readable medium of claim 11 wherein the spatiallysmoothing includes Gaussian blurring or median blurring.
 15. Themachine-readable medium of claim 11 wherein the thresholding involvesdynamically selecting threshold values using Otsu's method to minimizeintra-class intensity variance.
 16. A computer system executing programcode for guiding robotic surgery, the computer system comprising: amemory; and at least one processor operatively coupled with the memoryand executing program code from the memory comprising instructions for:receiving a series of cross-sectional slices of 3-dimensional (3D) spaceobtained from an optical coherence tomography (OCT) probe overbiological tissue, each slice of the series including a 2-dimensionalarray of intensity values; spatially smoothing the intensity values ineach slice to produce a corresponding blurred slice; thresholding eachblurred slice to create a corresponding segmented slice; performing aconnected-component analysis of each segmented slice to identify blobson said segmented slice; determining a span for each blob, the spanmeasuring a width across a respective segmented slice that the blobprojects; filtering out blobs on each segmented slice based on the spansof the blobs; edge detecting the filtered blobs on each segmented sliceto construct a corresponding edge detection slice; invoking a selectivemedian filter on the edge detection slices in order to construct a depthmap of a surface of the biological tissue; and guiding a robotic endeffector based on the depth map.
 17. The computer system of claim 16further comprising: removing from consideration a segmented slice whoselargest blob does not project at least 50% across said segmented slice.18. The computer system of claim 17 wherein the largest blob does notproject at least 75% across said segmented slice.
 19. The computersystem of claim 16 wherein the spatially smoothing includes Gaussianblurring or median blurring.
 20. The computer system of claim 16 whereinthe thresholding involves dynamically selecting threshold values usingOtsu's method to minimize intra-class intensity variance.